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Two-stage support vector machines to protein relative solvent accessibility prediction

Two-stage support vector machines to protein relative solven...

Proceedings of the 2004 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB'04

作者： Nguyen, Minh N. Rajapakse, Jagath C. BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore 639798

ISBN: (纸本)0780387287

bioinformatics techniques to Relative Solvent Accessibility (RSA) prediction are mostly single-stage approaches;they predict solvent accessibility of proteins by taking into account only the information available in amino acid sequences. In this paper, we propose to use Support Vector Machines (SVMs) as a second stage following the existing single-stage approaches for RSA prediction problem to improve the accuracy. The purpose of the second stage is to capture the contextual relationship of solvent accessibility elements in a neighborhood in determining the solvent accessibility at a particular site. We demonstrate our approach by introducing SVMs to the output of single-stage SVM classifier. Two-stage SVM approach achieves accuracies upto 90.4% and 90.2% on the Manesh dataset of 215 protein structures and the RS126 dataset of 126 nonhomologous globular proteins, respectively, which are better than the highest reported scores on both datasets to date. © 2004 ieee.

关键词： Proteins

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Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2189-2197页

作者： Nguyen-Quoc-Khanh Le Nguyen, Binh P. Taipei Med Univ Profess Master Program Artificial Intelligence Me Taipei 106 Taiwan Victoria Univ Wellington Sch Math & Stat Wellington 6012 New Zealand

Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the interruption in most of the cellular processes. Investigation on FMN's functions, therefore, can gain holistic understanding about human diseases and molecular information on drug targets. We proposed a deep learning model using a two-dimensional convolutional neural network and position specific scoring matrices that could identify FMN interacting residues with the sensitivity of 83.7 percent, specificity of 99.2 percent, accuracy of 98.2 percent, and Matthews correlation coefficients of 0.85 for an independent dataset containing 141 FMN binding sites and 1,920 non-FMN binding sites. The proposed method outperformed other previous studies using similar evaluation metrics. Our positive outcome can also promote the utilization of deep learning in dealing with various problems in bioinformatics and computational biology.

关键词： Proteins Deep learning Neural networks bioinformatics Microsoft Windows Computer architecture Feature extraction FMN binding site electron transport chains position specific scoring matrix convolutional neural networks deep learning

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Introduction to the Special Section on computational Methods in Systems biology

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第2期10卷 308-309页

作者： Fages, Francois Soliman, Sylvain Inria Paris Rocquencourt F-78153 Le Chesnay France

This special section contains the second series of journal articles made from a selection of papers presented at the Ninth International conference on computational Methods in Systems biology, CMSB 2011. CMSB is an annual series of conferences, initiated in 2003, on the design of computational methods for modeling and analyzing biological systems, networks, data, and on their applications to study cases. This issue contains the second part which is composed of three papers.“The Propagation Approach for Computing Biochemical Reaction Networks” by Thomas A. Henzinger and Maria Mateescu revisits the chemical master equation, the rate equation and a combination of both, with the concepts of propagation models and propagation data type for abstracting from the further implementation choices made in simulators.“Curvature Analysis of Cardiac Excitation Wavefronts” by Abhishek Murthy, Ezio Bartocci, Flavio H. Fenton, James Glimm, Richard A. Gray, Elizabeth M. Cherry, Scott A. Smolka, and Radu Grosu, which describes a parallel curvature analysis algorithm of cardiac excitation *** last paper, “Multiscale Modeling and Analysis of Planar Cell Polarity in the Drosophila Wing,” by Qian Gao, David Gilbert, Monika Heiner, Fei Liu, Daniele Maccagnola, and David Tree proposes the use of Hierarchically Colored Petri Nets to develop models of tissues at different spatial scales.

关键词： Biochemistry Biological Tissues biology Computing Cardiology Cellular Biophysics Chemical Reactions Master Equation Medical Computing Petri Nets Physiological Models Reaction Rate Constants System biology CMSB 2011 computational Method Design Biological System Modeling Biological System Analyzing Propagation Approach Biochemical Reaction Network Computing Chemical Master Equation Rate Equation Propagation Model Propagation Data Type Simulator Cardiac Excitation Wavefront Parallel Curvature Analysis Algorithm Planar Cell Polarity Multiscale Modeling Planar Cell Polarity Analysis Drosophila Wing Planar Cell Hierarchically Colored Petri Net Tissue Model Spatial Scale Special Issues And Sections Systems biology computational Modeling

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Analyzing gene expression data for childhood medulloblastoma survival with artificial neural networks

Analyzing gene expression data for childhood medulloblastoma...

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Proceedings of the 2004 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB'04

作者： Narayanan, Ajit Evangelia Keedwell, Ed Dept. of Artificial Intelligence University of Exeter Exeter EX4 4QF United Kingdom Bioinformatics Laboratory University of Exeter Exeter EX4 4QF United Kingdom Sch. of Eng. Comp. Sci. and Math. University of Exeter United Kingdom

ISBN: (纸本)0780387287

One of the major problems facing gene expression researchers is how to reduce the high dimensionality of gene expression data in the face of small sample sizes in comparison to the number of genes measured. In this paper we report on the application of a single layer neural network for reducing the number of originally measured genes from over 7000 to 64 through repeated application of thresholds on the weights linking genes to the class values of samples. This results in a small but informative gene set for the domain of brain cancer that can be further analyzed through the application of symbolic machine learning techniques (See5) and cluster analysis (Cluster and Treeview). © 2004 ieee.

关键词： Genes

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A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第5期8卷 1183-1195页

作者： Irurozki, Ekhine Calvo, Borja Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Gipuzkoa Spain

Haplotype data are especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and costly. computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach ( HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.

关键词： biology and genetics haplotype inference optimization

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Discovering Novel Cancer Therapies: A computational Modeling and Search Approach

Discovering Novel Cancer Therapies: A Computational Modeling...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： Mahoney, Arthur W. Smith, Brian G. Flann, Nicholas S. Podgorski, Gregory J. Utah State Univ Dept Comp Sci Logan UT 84322 USA Utah State Univ Ctr Integrated Bio Syst Dept Biol Logan UT 84322 USA

ISBN: (纸本)9781424417780

Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating rationally-designed treatments prevent the discovery of effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the cost of developing the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding interventions that are currently in use and more interestingly, discovering some new approaches that are counter intuitive yet effective.

关键词： cancer therapy Exploration Angiogenesis Search process Solid Tumor high fidelity computational modeling

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A Distance-Based Feature-Encoding Technique for Protein Sequence Classification in bioinformatics

A Distance-Based Feature-Encoding Technique for Protein Sequ...

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ieee International conference on computational intelligence and Cybernetics (ieee CyberneticsCom)

作者： Iqbal, Muhammad Jayed Faye, Ibrahima Said, Abas Md Samir, Brahim Belhaouari Univ Teknol PETRONAS Comp & Informat Syst Dept Tronoh Perak Malaysia Univ Teknol PETRONAS Fundamental & Appl Sci Dept Tronoh Perak Malaysia Univ Teknol PETRONAS Coll Sci Tronoh Perak Malaysia

ISBN: (纸本)9781467360531

bioinformatics has been emerging as a new research dimension since the last century by combining computer science and biology techniques for the automatic analysis of biological sequence data. The volume of the biological data gathered under different sequencing projects is increasing exponentially. These sequences contain extremely important information about genes, their structure and function. computational techniques which involve machine learning and pattern recognition are becoming very useful on bioinformatics data like DNA and protein. Protein classification into different groups could be used for knowing the structure or the function of unknown protein sequence. The process of classifying protein amino acid sequences into a family / superfamily is a very complex problem. However, from among other major issues in a protein classification, the critical one is an accurate representation of amino acid sequence during the feature extraction. In this work, we have proposed a distance-based feature-encoding method;the proposed technique has been tested with different classifiers, which have shown better results than the previously available techniques for superfamily classification of protein sequences. The maximum average classification accuracy obtained was 91.2%. The dataset used in the experiments was taken from the well known UniProtKB protein database.

关键词： bioinformatics Feature-encoding Data mining Superfamily Protein classification

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Exploratory Analysis of Topic Interests and Their Evolution in bioinformatics Research Using Semantic Text Mining and Probabilistic Topic Modeling

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ieee ACCESS 2022年 10卷 31480-31493页

作者： Gurcan, Fatih Cagiltay, Nergiz Ercil Karadeniz Tech Univ Fac Engn Dept Comp Engn TR-61080 Trabzon Turkey Atilim Univ Fac Engn Dept Software Engn TR-06830 Ankara Turkey

bioinformatics, which has developed rapidly in recent years with the collaborative contributions of the fields of biology and informatics, provides a deeper perspective on the analysis and understanding of complex biological data. In this regard, bioinformatics has an interdisciplinary background and a rich literature in terms of domain-specific studies. Providing a holistic picture of bioinformatics research by analyzing the major topics and their trends and developmental stages is critical for an understanding of the field. From this perspective, this study aimed to analyze the last 50 years of bioinformatics studies (a total of 71,490 articles) by using an automated text-mining methodology based on probabilistic topic modeling to reveal the main topics, trends, and the evolution of the field. As a result, 24 major topics that reflect the focuses and trends of the field were identified. Based on the discovered topics and their temporal tendencies from 1970 until 2020, the developmental periods of the field were divided into seven phases, from the "newborn" to the "wisdom" stages. Moreover, the findings indicated a recent increase in the popularity of the topics "Statistical Estimation", "Data Analysis Tools", "Genomic Data", "Gene Expression", and "Prediction". The results of the study revealed that, in bioinformatics studies, interest in innovative computing and data analysis methods based on artificial intelligence and machine learning has gradually increased, thereby marking a significant improvement in contemporary analysis tools and techniques based on prediction.

关键词： bioinformatics Market research biology Analytical models Genomics Proteins computational modeling bioinformatics corpus probabilistic topic modeling textual content analysis scientometric analysis bioinformatics topics and trends

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Predicting Cancer Driver Genes: Leveraging Graph Convolutional Networks and Ensemble Learning 21

Predicting Cancer Driver Genes: Leveraging Graph Convolution...

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21st ieee conference on computational intelligence in bioinformatics and computational biology, CIBCB 2024

作者： De Andrades, Renan Soares Recamonde-Mendoza, Mariana Ufrgs Bioinformatics Core Hcpa Institute of Informatics RS Porto Alegre Brazil

ISBN: (纸本)9798350356632

Cancer driver genes (CDGs) are crucial in cancer development and are key targets for treatment. This study proposes a novel approach utilizing Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), to predict CDGs by integrating protein-protein interaction (PPI) networks and multi-omics data. We optimized GCN hyperparameters across six different PPI networks from literature, which improved predictive accuracy. A consensus model was created using genes identified across all networks. We used ensemble-based methods, including majority voting and score aggregation, to identify high-probability CDGs. Comparisons showed that ensemble models outperformed individual models. Our approach identified several candidate genes, including some previously unclassified, as potential CDGs in various cancers. Notably, 33 genes were consistently predicted as CDGs across all networks, highlighting their potential importance in cancer development. This study highlights the effectiveness of GNNs for CDG prediction and the value of integrating multi-omics data and diverse PPI networks for advancing cancer genomics research and targeted therapy development. © 2024 ieee.

关键词： bioinformatics cancer driver genes machine learning multi-omics data

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Classification and Clustering in Metagenomics with Unified Data Management and computational Framework

Classification and Clustering in Metagenomics with Unified D...

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ieee International conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Rasheed, Zeehasham Rangwala, Huzefa Gillevet, Patrick George Mason Univ Dept Comp Sci Fairfax VA 22030 USA George Mason Univ Dept Environm Sci & Policy Microbiome Anal Ctr Fairfax VA 22030 USA

ISBN: (纸本)9781467327466;9781467327459

The new generation of genomic technologies have allowed researchers to determine the collective DNA of organisms co-existing as communities across different environments. There is a need for the computational approaches to analyze and annotate the large volumes of available sequence data from such microbial communities (metagenomes). In this work, we develop an efficient and accurate metagenome classification and clustering approaches that reduce the computational complexity associated with comparing sequences. Our empirical results show the strength of the developed approaches in comparison to state-of-the-art algorithms with regards to computational efficiency and accuracy. We are also developing a web based metabiome portal that contains different computational tools and is used for the scalable, accurate, and easy analysis of large volumes of data available for the human metabiome.

关键词： metagenomics classification clustering species diversity hashing system biology

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